An instrumented timed up and go: the added value of an accelerometer for identifying fall risk in idiopathic fallers

Toward Automating Clinical Assessments: A Survey of the Timed Up and Go

Older adults often suffer from functional impairments that affect their ability to perform everyday tasks. To detect the onset and changes in abilities, healthcare professionals administer standardized assessments. Recently, technology has been utilized to complement these clinical assessments to gain a more objective and detailed view of functionality. In the clinic and at home, technology is able to provide more information about patient performance and reduce subjectivity in outcome measures. The timed up and go (TUG) test is one such assessment recently instrumented with technology in several studies, yielding promising results toward the future of automating clinical assessments. Potential benefits of technological TUG implementations include additional performance parameters, generated reports, and the ability to be self-administered in the home. In this paper, we provide an overview of the TUG test and technologies utilized for TUG instrumentation. We then critically review the technological advancements and follow up with an evaluation of the benefits and limitations of each approach. Finally, we analyze the gaps in the implementations and discuss challenges for future research toward automated self-administered assessment in the home.

Kinematic parameters to evaluate functional performance of sit-to-stand and stand-to-sit transitions using motion sensor devices: a systematic review

Clinicians commonly use questionnaires and tests based on daily life activities to evaluate physical function. However, the outcomes are usually more qualitative than quantitative and subtle differences are not detectable. In this review, we aim to assess the role of body motion sensors in physical performance evaluation, especially for the sit-to-stand and stand-to-sit transitions. In total, 53 full papers and conference abstracts on related topics were included and 16 different parameters related to transition performance were identified as potentially meaningful to explain certain disabilities and impairments. Transition duration is the most used to evaluate chair-related tests in real clinical settings. High-fall-risk fallers and frail subjects presented longer and more variable transition duration. Other kinematic parameters have also been highlighted in the literature as potential means to detect age-related impairments. In particular, vertical linear velocity and trunk tilt range were able to differentiate between different frailty levels. Frequency domain measures such as spectral edge frequency were also higher for elderly fallers. Lastly, approximate entropy values were larger for subjects with Parkinson's disease and were significantly reduced after treatment. This information could help clinicians in their evaluations as well as in prescribing a physical fitness program to correct a specific deficit.

Objective biomarkers of balance and gait for Parkinson's disease using body-worn sensors

Balance and gait impairments characterize the progression of Parkinson's disease (PD), predict the risk of falling, and are important contributors to reduced quality of life. Advances in technology of small, body-worn, inertial sensors have made it possible to develop quick, objective measures of balance and gait impairments in the clinic for research trials and clinical practice. Objective balance and gait metrics may eventually provide useful biomarkers for PD. In fact, objective balance and gait measures are already being used as surrogate endpoints for demonstrating clinical efficacy of new treatments, in place of counting falls from diaries, using stop-watch measures of gait speed, or clinical balance rating scales. This review summarizes the types of objective measures available from body-worn sensors. The metrics are organized based on the neural control system for mobility affected by PD: postural stability in stance, postural responses, gait initiation, gait (temporal-spatial lower and upper body coordination and dynamic equilibrium), postural transitions, and freezing of gait. However, the explosion of metrics derived by wearable sensors during prescribed balance and gait tasks, which are abnormal in individuals with PD, do not yet qualify as behavioral biomarkers, because many balance and gait impairments observed in PD are not specific to the disease, nor have they been related to specific pathophysiologic biomarkers. In the future, the most useful balance and gait biomarkers for PD will be those that are sensitive and specific for early PD and are related to the underlying disease process.

Quantitative assessment of upper limb motor function in Multiple Sclerosis using an instrumented Action Research Arm Test

BACKGROUND

Arm impairment in Multiple Sclerosis (MS) is commonly assessed with clinical scales, such as Action Research Arm Test (ARAT) which evaluates the ability to handle and transport smaller and larger objects. ARAT provides a complete upper limb assessment, as it considers both proximal arm and hand, but suffers from subjectivity and poor sensitivity to mild impairment. In this study an instrumented ARAT is proposed to overcome these limitations and supplement the assessment of arm function in MS.

METHODS

ARAT was executed by 12 healthy volunteers and 21 MS subjects wearing a single inertial sensor on the wrist. Accelerometers and gyroscopes signals were used to calculate the duration of each task and its sub-phases (reaching, manipulation, transport, release and return). A jerk index was computed to quantify movement smoothness. For each parameter, z-scores were calculated to analyze the deviation from normative data. MS subjects were clinically assessed with ARAT score, Nine-Hole Peg test (9HPT) and Fahn Tremor Rating Scale (FTRS).

RESULTS

ARAT tasks executed by MS patients were significantly slower (duration increase: 70%) and less smooth (jerk increase: 16%) with respect to controls. These anomalies were mainly related to manipulation, transport and release sub-movements, with the former showing the greatest alterations. A statistically significant decrease in movement velocity and smoothness was also noticed in patients with normal ARAT score. Z-scores related to duration and jerk were strongly correlated with ARAT rating (r < -0.80, p < 0.001) and 9HPT (r < -0.75, p < 0.001) and were significantly different among MS sub-groups with different levels of arm impairments (p < 0.001). Moreover, Z-score related to manipulation-phase jerk was significantly correlated with the FTRS rating of intention tremor (r = 0.84, p < 0.001).

CONCLUSIONS

The present study showed that the proposed method is able to discriminate between control and MS groups and to reveal subtle arm alterations not detectable from ARAT score. Validity was shown by high correlations between instrumental variables and clinical ratings. These results suggested that instrumented ARAT could be a valid quick and easy-to-use method for a sensitive quantification of arm function in MS. Inclusion of finger-mounted sensors could complement present findings and provide further indications about hand function in MS.

Quantifying Individual Components of the Timed Up and Go Using the Kinect in People Living With Stroke

Background. The Microsoft Kinect presents a simple, inexpensive, and portable method of examining the independent components of the Timed Up and Go (TUG) without any intrusion on the patient. Objective. This study examined the reliability of these measures, and whether they improved prediction of performance on common clinical tests. Methods. Thirty individuals with stroke completed 4 clinical assessments, including the TUG, 10-m walk test (10MWT), Step Test, and Functional Reach test on 2 testing occasions. The TUG was assessed using the Kinect to determine 7 different functional components. Test–retest reliability was assessed using intraclass correlation coefficient (ICC), redundancy using Spearman’s correlation, and score prediction on the clinical tests using multiple regression. Results. All Kinect-TUG variables possessed excellent reliability (ICC(2,k) > 0.90) except trunk flexion angle (ICC = 0.73). Trunk flexion angle and first step length were nonredundant with total TUG time. When predicting 10MWT and Step Test scores, adding step length into regression models comprising age and total TUG time improved model performance by 7% ( P <.01) and 6% ( P =.03), respectively. Specifically, an interquartile range increase in first step length (0.19 m) was associated with a 0.15 m/s faster gait speed and 1.8 more repetitions on the Step Test. These effect sizes were comparable to our minimal detectable change scores of 0.17 m/s for gait speed and 1.71 repetitions for the Step Test. Conclusions. Using the Kinect to independently assess the multiple components of the TUG may provide reliable and clinically useful information. This could enable efficient and information-rich large-scale assessments of physical deficits following stroke.

Consistency of gait characteristics as determined from acceleration data collected at different trunk locations

Estimates of gait characteristics may suffer from errors due to discrepancies in accelerometer location. This is particularly problematic for gait measurements in daily life settings, where consistent sensor positioning is difficult to achieve. To address this problem, we equipped 21 healthy adults with tri-axial accelerometers (DynaPort MiniMod, McRoberts) at the mid and lower lumbar spine and anterior superior iliac spine (L2, L5 and ASIS) while continuously walking outdoors back and forth (20 times) over a distance of 20 m, including turns. We compared 35 gait characteristics between sensor locations by absolute agreement intra-class correlations (2, 1; ICC). We repeated these analyses after applying a new method for off-line sensor realignment providing a unique definition of the vertical and, by symmetry optimization, the two horizontal axes. Agreement between L2 and L5 after realignment was excellent (ICC>0.9) for stride time and frequency, speed and their corresponding variability and good (ICC>0.7) for stride regularity, movement intensity, gait symmetry and smoothness and for local dynamic stability. ICC values benefited from sensor realignment. Agreement between ASIS and the lumbar locations was less strong, in particular for gait characteristics like symmetry, smoothness, and local dynamic stability (ICC generally<0.7). Unfortunately, this lumbar-ASIS agreement did not benefit consistently from sensor realignment. Our findings show that gait characteristics are robust against limited repositioning error of sensors at the lumbar spine, in particular if our off-line realignment is applied. However, larger positioning differences (from lumbar positions to ASIS) yield less consistent estimates and should hence be avoided.

Association between performance on Timed Up and Go subtasks and mild cognitive impairment: further insights into the links between cognitive and motor function

OBJECTIVES

To assess whether different Timed Up and Go (TUG) subtasks are affected differently in older adults with mild cognitive impairment (MCI) and are specific to different cognitive abilities.

DESIGN

Cross-sectional.

SETTING

Community and home.

PARTICIPANTS

Older adults without dementia (N = 347; mean age 83.6 ± 3.5, 75% female, 19.3% with MCI) participating in the Rush Memory and Aging Project.

MEASUREMENTS

Subjects wore a small, light-weight sensor that measured acceleration and angular velocity while they performed the instrumented TUG (iTUG). Measures of iTUG were derived from four subtasks (walking, turning, sit-to-stand, stand-to-sit) and compared between participants with MCI and those with no cognitive impairment.

RESULTS

Participants with no cognitive impairment and those with MCI did not differ in age (P = .90), sex (P = .80), years of education (P = .48) or time to complete the TUG (no cognitive impairment 7.6 ± 3.7 seconds; MCI 8.4 ± 3.7 seconds; P = .12). Participants with MCI had less walking consistency (P = .009), smaller pitch range during transitions (P = .005), lower angular velocity during turning (P = .04) and required more time to complete the turn-to-walk (P = .04). Gait consistency was correlated with perceptual speed (P = .01), and turning was correlated with perceptual speed (P = .02) and visual-spatial abilities (P = .049).

CONCLUSION

Mild cognitive impairment is associated with impaired performance on iTUG subtasks that cannot be identified when simply measuring overall duration of performance. Distinctive iTUG tasks were related to particular cognitive domains, demonstrating the specificity of motor-cognitive interactions. Using a single sensor worn on the body for quantification of mobility may facilitate understanding of late-life gait impairments and their interrelationship with cognitive decline.

Differences in trunk accelerometry between frail and non-frail elderly persons in functional tasks

Background

Physical conditions through gait and other functional task are parameters to consider for frailty detection. The aim of the present study is to measure and describe the variability of acceleration, angular velocity and trunk displacement in the ten meter Extended Timed Get-Up-and-Go test in two groups of frail and non-frail elderly people through instrumentation with the iPhone4® smartphone. Secondly, to analyze the differences and performance of the variance between the study groups (frail and non-frail).

This is a cross-sectional study of 30 subjects aged over 65 years, 14 frail subjects and 16 non-frail subjects.

Results

The highest difference between groups in the Sit-to-Stand and Stand-to-Sit subphases was in the y axis (vertical vector). The minimum acceleration in the Stand-to-Sit phase was -2.69 (-4.17 / -0.96) m/s² frail elderly versus -8.49 (-12.1 / -5.23) m/s² non-frail elderly, p < 0.001. In the Gait Go and Gait Come subphases the biggest differences found between the groups were in the vertical axis: -2.45 (-2.77 /-1.89) m/s² frail elderly versus -5.93 (-6.87 / -4.51) m/s² non-frail elderly, p < 0.001. Finally, with regards to the turning subphase, the statistically significant differences found between the groups were greater in the data obtained from the gyroscope than from the accelerometer (the gyroscope data for the mean maximum peak value for Yaw movement angular velocity in the frail elderly was specifically 25.60°/s, compared to 112.8°/s for the non-frail elderly, p < 0.05).

Conclusions

The inertial sensor fitted in the iPhone4® is capable of studying and analyzing the kinematics of the different subphases of the Extended Timed Up and Go test in frail and non-frail elderly people. For the Extended Timed Up and Go test, this device allows more sensitive differentiation between population groups than the traditionally used variable, namely time.

Associations between quantitative mobility measures derived from components of conventional mobility testing and Parkinsonian gait in older adults

Objective

To provide objective measures which characterize mobility in older adults assessed in the community setting and to examine the extent to which these measures are associated with parkinsonian gait.

Methods

During conventional mobility testing in the community-setting, 351 ambulatory non-demented Memory and Aging Project participants wore a belt with a whole body sensor that recorded both acceleration and angular velocity in 3 directions. We used measures derived from these recordings to quantify 5 subtasks including a) walking, b) transition from sit to stand, c) transition from stand to sit, d) turning and e) standing posture. Parkinsonian gait and other mild parkinsonian signs were assessed with a modified version of the original Unified Parkinson’s Disease Rating Scale (mUPDRS).

Results

In a series of separate regression models which adjusted for age and sex, all 5 mobility subtask measures were associated with parkinsonian gait and accounted for 2% to 32% of its variance. When all 5 subtask measures were considered in a single model, backward elimination showed that measures of walking sit to stand and turning showed independent associations with parkinsonian gait and together accounted for more than 35% of its variance. Cross-validation using data from a 2^nd group of 258 older adults showed similar results. In similar analyses, only walking was associated with bradykinesia and sway with tremor.

Interpretation

Quantitative mobility subtask measures vary in their associations with parkinsonian gait scores and other parkinsonian signs in older adults. Quantifying the different facets of mobility has the potential to facilitate the clinical characterization and understanding the biologic basis for impaired mobility in older adults.

A comparison of cross-sectional and prospective algorithms for falls risk assessment

Falls are the most common cause of injury and hospitalization and one of the principal causes of death and disability in older adults worldwide. Accurate identification of patients at risk of falls could lead to timely medical intervention, reducing the incidence of falls related injuries along with associated costs. The current best practice for studies of falls and falls risk recommends the use of prospective follow-up data. However, the majority of studies reporting sensor based methods for assessment of falls risk employ cross-sectional falls data (falls history). The purpose of this study was to compare the performance of sensor based falls risk assessment algorithms derived from cross-sectional (N=909) and prospective (N=259) datasets in terms of false positive rate. The utility of any classification algorithm is clearly limited by a high false positive rate. An estimate of the false positive rate for both cross-sectional and prospective algorithms was determined using an inertial sensor data set of 611 TUG tests from 55 healthy control subjects, with no history of falls. We aimed to determine which falls risk assessment algorithm is more effective at classifying falls risk in healthy control subjects. The cross-sectional algorithm correctly classified 94.11% of tests, while the prospective algorithm, correctly classified 79.38% of tests. Results suggest that sensor based falls risk assessment algorithms generated using cross-sectional falls data, may be more effective than those generated using prospective data in classifying healthy controls and reducing associated false positives.

Identifying axial and cognitive correlates in patients with Parkinson's disease motor subtype using the instrumented Timed Up and Go

Parkinson's disease (PD) is clinically highly heterogeneous, often divided into tremor dominant (TD) and postural instability gait difficulty (PIGD). To better understand these subtypes and to help stratify patients, we applied an objective marker, i.e., an instrumented version of the traditional "Timed Up and Go" test (iTUG). It is not known whether the iTUG is sensitive to PD motor phenotypes or what are its behavioral and cognitive correlates. Subjects performed the iTUG wearing a body-fixed sensor. Subcomponents were studied including walking, transitions and turning. Gait, balance and cognitive function and the associations between iTUG, behavioral and cognitive domains were assessed. We also compared two representative subtypes, with minimal symptom overlap, referred to here as predominant PIGD (p-PIGD) and predominant TD (p-TD). One hundred and six patients with PD performed the iTUG. Significant correlations were found between iTUG measures and the PIGD score, but not with TD score. Thirty p-PIGD and 31 p-TD patients were identified. Both groups were similar with respect to age and disease duration (p > 0.75). The p-PIGD patients took significantly longer to complete the iTUG (p = 0.026), used more steps (p = 0.031), albeit with similar step duration (p = 0.936). In the sit-to-stand transition, the p-PIGD patients exhibited lower anterior-posterior jerk (p = 0.04) and lower pitch range (p = 0.012). During the turn, the p-PIGD patients had a lower yaw amplitude (p < 0.038). Cognitive domains were correlated with iTUG measures in the p-PIGD patients, but not in the p-TD. These findings demonstrate that a single sensor can identify axial and cognitive correlates using subcomponents of the iTUG and reveals subtle alterations between the PD motor subtypes.

Differences in trunk kinematic between frail and nonfrail elderly persons during turn transition based on a smartphone inertial sensor

OBJECTIVE

Firstly, to, through instrumentation with the iPhone4 smartphone, measure and describe variability of tridimensional acceleration, angular velocity, and displacement of the trunk in the turn transition during the ten-meter Extended Timed-Get-up-and-Go test in two groups of frail and physically active elderly persons. Secondly, to analyse the differences and performance of the variance between the study groups during turn transition (frail and healthy).

DESIGN

This is a cross-sectional study of 30 subjects over 65 years, 14 frail subjects, and 16 healthy subjects.

RESULTS

Significant differences were found between the groups of elderly persons in the accelerometry (P < 0.01) and angular displacement variables (P < 0.05), obtained in the kinematic readings of the trunk during the turning transitions. The results obtained in this study show a series of deficits in the frail elderly population group.

CONCLUSIONS

The inertial sensor found in the iPhone4 is able to study and analyse the kinematics of the turning transitions in frail and physically active elderly persons. The accelerometry values for the frail elderly are lower than the physically active elderly, whilst variability in the readings for the frail elderly is also lower than the control group.

Instrumenting the balance error scoring system for use with patients reporting persistent balance problems after mild traumatic brain injury

OBJECTIVE

To determine whether alterations to the Balance Error Scoring System (BESS), such as modified conditions and/or instrumentation, would improve the ability to correctly classify traumatic brain injury (TBI) status in patients with mild TBI with persistent self-reported balance complaints.

DESIGN

Cross-sectional study.

SETTING

Outpatient clinic.

PARTICIPANTS

Subjects (n=13; age, 16.3±2y) with a recent history of concussion (mild TBI group) and demographically matched control subjects (n=13; age, 16.7±2y; control group).

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURES

Outcome measures included the BESS, modified BESS, instrumented BESS, and instrumented modified BESS. All subjects were tested on the noninstrumented BESS and modified BESS and were scored by visual observation of instability in 6 and 3 stance conditions, respectively. Instrumentation of these 2 tests used 1 inertial sensor with an accelerometer and gyroscope to quantify bidirectional body sway.

RESULTS

Scores from the BESS and the modified BESS tests were similar between groups. However, results from the instrumented measures using the inertial sensor were significantly different between groups. The instrumented modified BESS had superior diagnostic classification and the largest area under the curve when compared with the other balance measures.

CONCLUSIONS

A concussion may disrupt the sensory processing required for optimal postural control, which was measured by sway during quiet stance. These results suggest that the use of portable inertial sensors may be useful in the move toward more objective and sensitive measures of balance control postconcussion, but more work is needed to increase sensitivity.

Differences in Trunk Accelerometry Between Frail and Nonfrail Elderly Persons in Sit-to-Stand and Stand-to-Sit Transitions Based on a Mobile Inertial Sensor

BACKGROUND

Clinical frailty syndrome is a common geriatric syndrome, which is characterized by physiological reserve decreases and increased vulnerability. The changes associated to ageing and frailties are associated to changes in gait characteristics and the basic functional capacities. Traditional clinical evaluation of Sit-to-Stand (Si-St) and Stand-to-Sit (St-Si) transition is based on visual observation of joint angle motion to describe alterations in coordination and movement pattern. The latest generation smartphones often include inertial sensors with subunits such as accelerometers and gyroscopes, which can detect acceleration.

OBJECTIVE

Firstly, to describe the variability of the accelerations, angular velocity, and displacement of the trunk during the Sit-to-Stand and Stand-to-Sit transitions in two groups of frail and physically active elderly persons, through instrumentation with the iPhone 4 smartphone. Secondly, we want to analyze the differences between the two study groups.

METHODS

A cross-sectional study that involved 30 subjects over 65 years, 14 frail and 16 fit subjects. The participants were classified with frail syndrome by the Fried criteria. Linear acceleration was measured along three orthogonal axes using the iPhone 4 accelerometer. Each subject performed up to three successive Si-St and St-Si postural transitions using a standard chair with armrest.

RESULTS

Significant differences were found between the two groups of frail and fit elderly persons in the accelerometry and angular displacement variables obtained in the kinematic readings of the trunk during both transitions.

CONCLUSIONS

The inertial sensor fitted in the iPhone 4 is able to study and analyze the kinematics of the Si-St and St-Si transitions in frail and physically active elderly persons. The accelerometry values for the frail elderly are lower than for the physically active elderly, while variability in the readings for the frail elderly is also lower than for the control group.

Review of fall risk assessment in geriatric populations using inertial sensors

Background

Falls are a prevalent issue in the geriatric population and can result in damaging physical and psychological consequences. Fall risk assessment can provide information to enable appropriate interventions for those at risk of falling. Wearable inertial-sensor-based systems can provide quantitative measures indicative of fall risk in the geriatric population.

Methods

Forty studies that used inertial sensors to evaluate geriatric fall risk were reviewed and pertinent methodological features were extracted; including, sensor placement, derived parameters used to assess fall risk, fall risk classification method, and fall risk classification model outcomes.

Results

Inertial sensors were placed only on the lower back in the majority of papers (65%). One hundred and thirty distinct variables were assessed, which were categorized as position and angle (7.7%), angular velocity (11.5%), linear acceleration (20%), spatial (3.8%), temporal (23.1%), energy (3.8%), frequency (15.4%), and other (14.6%). Fallers were classified using retrospective fall history (30%), prospective fall occurrence (15%), and clinical assessment (32.5%), with 22.5% using a combination of retrospective fall occurrence and clinical assessments. Half of the studies derived models for fall risk prediction, which reached high levels of accuracy (62-100%), specificity (35-100%), and sensitivity (55-99%).

Conclusions

Inertial sensors are promising sensors for fall risk assessment. Future studies should identify fallers using prospective techniques and focus on determining the most promising sensor sites, in conjunction with determination of optimally predictive variables. Further research should also attempt to link predictive variables to specific fall risk factors and investigate disease populations that are at high risk of falls.

Using a body-fixed sensor to identify subclinical gait difficulties in older adults with IADL disability: maximizing the output of the timed up and go

Objective

The identification and documentation of subclinical gait impairments in older adults may facilitate the appropriate use of interventions for preventing or delaying mobility disability. We tested whether measures derived from a single body-fixed sensor worn during traditional Timed Up and Go (TUG) testing could identify subclinical gait impairments in community dwelling older adults without mobility disability.

Methods

We used data from 432 older adults without dementia (mean age 83.30±7.04 yrs, 76.62% female) participating in the Rush Memory and Aging Project. The traditional TUG was conducted while subjects wore a body-fixed sensor. We derived measures of overall TUG performance and different subtasks including transitions (sit-to-stand, stand-to-sit), walking, and turning. Multivariate analysis was used to compare persons with and without mobility disability and to compare individuals with and without Instrumental Activities of Daily Living disability (IADL-disability), all of whom did not have mobility disability.

Results

As expected, individuals with mobility disability performed worse on all TUG subtasks (p<0.03), compared to those who had no mobility disability. Individuals without mobility disability but with IADL disability had difficulties with turns, had lower yaw amplitude (p<0.004) during turns, were slower (p<0.001), and had less consistent gait (p<0.02).

Conclusions

A single body-worn sensor can be employed in the community-setting to complement conventional gait testing. It provides a wide range of quantitative gait measures that appear to help to identify subclinical gait impairments in older adults.

Electromyographic responses during time get up and go test in water (wTUG)

The aim of this study was to use sEMG to measure the neuromuscular activity during the TUG task in water, and compare this with the responses for the same task on land. Ten healthy subjects [5 males and 5 females [mean ± SD]: age, 22.0 ± 3.1 yr; body mass, 63.9 ± 17.2 kg. A telemetry EMG system was used on the following muscles on the right side of the body: the quadriceps – rectus femoris [RF], long head of the biceps femoris [BF], tibialis anterior [TA], gastrocnemius medialis [GM], soleus [SOL], rectus abdominis [RA] and erector spinae [ES]. Each subject performed the TUG test three times with five minutes recover between trials in water and on dry land. The % MVC was significantly different (p < 0.05) for majority of the muscles tested during the TUG water compared to dry land. % MVC of RF [p = 0.003, t = 4.07]; BF [p = 0.000, t = 6.8]; TA [p = 0.005, t = 5.9]; and SOL [p = 0.048, t = 1.98]; RA [p = 0.007, t = 3.45]; and ES [p = 0.004, t = 3.78]. The muscle activation of the trunk and the lower limb [VM RF, BF, TA, GM and SOL] were lower in water compared to dry land, when performing a TUG test.

Effect of a plantar perceptual learning task on walking stability in the elderly: a randomized controlled trial

OBJECTIVE

To determine whether the plantar perceptual learning task, using a hardness discrimination training, efficiently improves walking stability in the elderly.

DESIGN

A randomized controlled trial.

SETTING

Elder day-care center.

PARTICIPANTS

Eighty-six elderly people (73.84 SD 5.98 years) who went to an elder day-care center were randomly assigned evenly to either an intervention or a control group.

INTERVENTION

The intervention group performed a task to discriminate hardness differences while standing on sponge mats of different levels of hardness. The control group underwent the same task except that they were not instructed to discriminate hardness levels of the mats. The tasks were carried out over a four-week period for 10 days for both groups.

OUTCOME MEASURES

Outcome was assessed by determining root mean squares of trunk acceleration during walking.

RESULTS

Plantar perception was significantly improved in the intervention group after training (F = 26.24, p < 0.01). In addition, changes in root mean square values of acceleration were significantly greater after training in the intervention group (medial-lateral, 0.36 SD 0.26; vertical, 0.32 SD 0.24; anterio-posterior, 0.26 SD 0.24) than in the control group (medial-lateral, 0.14 SD 0.28, vertical, 0.16 SD 0.35, anterio-posterior, 0.12 SD 0.29) (p < 0.05). Changes in walking speed were not significantly different (p = 0.13) between the intervention (0.06 SD 0.13) and control groups (0.02 SD 0.14).

CONCLUSION

The plantar perceptual learning task might efficiently stabilize postural control during walking in the elderly.

Quantification of motor impairment in Parkinson's disease using an instrumented timed up and go test

The Timed Up and Go (TUG) test is a clinical test to assess mobility in Parkinson's disease (PD). It consists of rising from a chair, walking, turning, and sitting. Its total duration is the traditional clinical outcome. In this study an instrumented TUG (iTUG) was used to supplement the quantitative information about the TUG performance of PD subjects: a single accelerometer, worn at the lower back, was used to record the acceleration signals during the test and acceleration-derived measures were extracted from the recorded signals. The aim was to select reliable measures to identify and quantify the differences between the motor patterns of healthy and PD subjects; in order to do so, besides comparing each measure individually to find significant group differences, feature selection and classification were used to identify the distinctive motor pattern of PD subjects. A subset of three features (two from Turning, one from the Sit-to-Walk component), combined with an easily-interpretable classifier (Linear Discriminant Analysis), was found to have the best accuracy in discriminating between healthy and early-mild PD subjects. These results suggest that the proposed iTUG can characterize PD motor impairment and, hence, may be used for evaluation, and, prospectively, follow-up, and monitoring of disease progression.

Sit-stand and stand-sit transitions in older adults and patients with Parkinson's disease: event detection based on motion sensors versus force plates

BACKGROUND

Motion sensors offer the possibility to obtain spatiotemporal measures of mobility-related activities such as sit-stand and stand-sit transitions. However, the application of new sensor-based methods for assessing sit-stand-sit performance requires the detection of crucial events such as seat on/off in the sensor-based data. Therefore, the aim of this study was to evaluate the agreement of detecting sit-stand and stand-sit events based on a novel body-fixed-sensor method with a force-plate based analysis.

METHODS

Twelve older adults and 10 patients with mild to moderate Parkinson's disease with mean age of 70 years performed sit-stand-sit movements while trunk movements were measured with a sensor-unit at vertebrae L2-L4 and reaction forces were measured with separate force plates below the feet and chair. Movement onsets and ends were determined. In addition, seat off and seat on were determined based on forces acting on the chair. Data analysis focused on the agreement of the timing of sit-stand and stand-sit events as detected by the two methods.

RESULTS

For the start and end of standing-up, only small delays existed for the start of forward trunk rotation and end of backward trunk rotation compared to movement onset/end as detected in the force-plate data. The end of forward trunk rotation had a small and consistent delay compared to seat off, whereas during sitting-down, the end of forward trunk rotation occurred earlier in relation to seat on. In detecting the end of sitting-down, backward trunk rotation ended after reaching the minimum in the below-feet vertical force signal. Since only small time differences existed between the two methods for detecting the start of sitting-down, longer movement durations were found for the sensor-based method. Relative agreement between the two methods in assessing movement duration was high (i.e. ICCs ≥ 0.75), except for duration of standing-up in the Parkinson's patients (ICC = 0.61).

CONCLUSIONS

This study demonstrated high agreement of body-fixed-sensor based detection of sit-stand and stand-sit events with that based on force plates in older adults and patients with mild to moderate Parkinson's disease. Further development and testing is needed to establish reliability for unstandardized performance in clinical and home settings.